Traffic state prediction is a crucial component of Intelligent Transportation Systems (ITS) and has many applications in traffic control and management. Various statistical and machine learning methods, ranging from traditional techniques [1]}, [2]}, [3]} to deep learning models [4]}, [5]}, [6]}, [7]}, have been applied to improve the prediction accuracy. The spatial-temporal resolution of prediction has also evolved from a single timestep on one link [8]}, [9]}, [10]} to multiple timesteps on the entire road network [11]}, [12]}, [13]}, [14]}, [7]}. In this work, we focus on network-wide, multi-step prediction models as they represent the latest advancements regarding the subject.
